The increased availability of labeled X-ray image archives (e.g. ChestX-ray14 dataset) has triggered a growing interest in deep learning techniques. To provide better insight into the different approaches, and their applications to chest X-ray classification, we investigate a powerful network architecture in detail: the ResNet-50. Building on prior work in this domain, we consider transfer learning with and without fine-tuning as well as the training of a dedicated X-ray network from scratch. To leverage the high spatial resolution of X-ray data, we also include an extended ResNet-50 architecture, and a network integrating non-image data  in the classification process. In a concluding experiment, we also investigate multiple ResNet depths (i.e. ResNet-38 and ResNet-101). In a systematic evaluation, using 5-fold re-sampling and a multi-label loss function, we compare the performance of the different approaches for pathology classification by ROC statistics and analyze differences between the classifiers using rank correlation. Overall, we observe a considerable spread in the achieved performance and conclude that the X-ray-specific ResNet-38, integrating non-image data yields the best overall results. Furthermore, class activation maps are used to understand the classification process, and a detailed analysis of the impact of non-image features is provided. The Queen Alexandra Hospital is probably not the only hospital having problems with providing expert readings for every CXR. Growing populations and increasing life expectancies are expected to drive an increase in demand for CXR readings.In the United Kingdom, the care quality commission recently reported that – over the preceding 12 months – a total of 23,000 chest X-rays (CXRs) were not formally reviewed by a radiologist or clinician at Queen Alexandra Hospital alone. Furthermore, three patients with lung cancer suffered significant harm because their CXRs had not been properly assessed5. In addition, the medical image processing field is vividly exploring deep learning. However, one major problem in the medical domain is the availability of large datasets with reliable ground-truth annotation. Therefore, transfer learning approaches, as proposed by Bar et al.6, were often considered to overcome such problems.In computer vision, deep learning has already shown its power for image classification with superhuman accuracy7 and the ChestX-ray14 dataset from the National Institutes of Health (NIH) Clinical Center8. Figure et al.8 using convolution neural networks (CNNs) from the computer vision domain, several research groups have begun to address the application of CNNs for CXR classification. In the work of Yao et al.9, they presented a combination of a CNN and a recurrent neural network to exploit label dependencies. As a CNN backbone, they used a DenseNet10 model which was adapted and trained entirely on X-ray data. Li et al.11 presented a framework for pathology classification and localization using CNNs. More recently, Rajpurkar et al.12 proposed transfer-learning with fine tuning, using a DenseNet-12110, which raised the AUC results on ChestX-ray14 for multi-label classification even higher.Two larger X-ray datasets have recently become available: The CXR dataset from Open-iet al.11 reported 5-fold cross-validated results. In contrast to these results, our experiments (Sec. 3) demonstrate that performance of a network depends significantly on the selected split. To have a fair comparison, Wang et al.8 released an official split later. Yao et al.13 and Guendel et al.14 reported results for this official split. While Guendel et al.14 hold the state-of-the-art results in all fourteen classes with a location-aware DenseNet-121.Unfortunately, a faithful comparison of approaches remains difficult. Most reported results were obtained with differing experimental setups. This includes (among others) the employed network architecture, loss function and data augmentation. In addition, differing dataset splits were used and only Li weight initialization, pre-training and transfer learning (Section 2.1)network architectures such as ResNet-50 with large input size (Section 2.2)non-image features such as age, gender, and view position (Section 2.3)To provide better insights into the effects of distinct design decisions for deep learning, we perform a systematic evaluation using a 5-fold re-sampling scheme. We empirically analyze three major topics:Prior work on ChestX-ray14 has been limited to the analysis of image data. In clinical practice however, radiologists employ a broad range of additional features during the diagnosis. To leverage the complete information of the dataset , we propose in Section 2.3 a novel architecture integrating this information in addition to the learned image representation.l using N training sample-label pairs . We encode “No Finding” as an explicit additional label and hence have M = 15 labels. After an initial investigation of weighting loss functions such as positive/negative balancing8 and class balancing, we noticed no significant difference and decided to employ the class-averaged binary cross entropy (BCE) as our objective:In the following, we cast pathology detection as a multi-label classification problem. All images 10, we focus in our experiments on the ResNet-50 architecture15. To adapt the network to the new task, we replace the last dense layer of the original architecture with a new dense layer matching the number of labels and add a sigmoid activation function for our multi-label problem  and fine-tuning (FT) in the transfer-learning approach.We investigate two distinct initialization strategies for the ResNet-50. First, we follow the scheme described by He et al.16, a source domain ft(⋅) in A major drawback in medical image processing with deep learning is the limited size of datasets compared to the computer vision domain. Hence, training a CNN from scratch is often not feasible. One solution is transfer-learning. Following the notation in the work of Pan 18, the pre-trained network is used as a feature extractor, and only the weights of the last (classifier) layer are adapted. In fine-tuning, one chooses to re-train one or more layers with samples from the new domain. For both approaches, we use the weights of a ResNet-50 network trained on ImageNet as a starting point19. In our fine-tuning experiment, we retrained all conv-layers as shown in Table Employing an off-the-shelf approachIn addition to the original ResNet-50 architecture, we employ two variants: First, we reduce the number of input channels to one (the ResNet-50 is designed for the processing of RGB images from the ImageNet dataset), which should facilitate the training of an X-ray specific CNN. Second, we increase the input size by a factor of two (i.e. 448 × 448). To keep the model architectures similar, we only add a new max-pooling layer after the first bottleneck block. This max-pooling layer has the same parameters as the “pooling1” layer . In Fig. Finally, we investigate different model depths with the best performing setup. First, we implement a shallower ResNet-38 where we reduce the number of bottleneck blocks for conv2_x, conv3_x, and conv4_x down to two, two, and three, respectively. Secondly, we also test the ResNet-101 and increased the number of conv_3 blocks from 5 to 22 compare to the ResNet-50.ChestX-ray14 contains information about the patient age, gender, and view position (i.e. if the X-ray image is acquired posterior-anterior (PA) or anterior-posterior (AP)). Radiologists use information beyond the image to conclude which pathologies are present or not. The view position changes the expected position of organs in the X-ray images . In addition, organs (e.g. the heart) are magnified in an AP projection as the distance to the detector is increased.As illustrated in Fig. et al.8 performed a simple preprocessing based on the encoded display settings while the pixel depth was reduced to 8-bit. In addition, each image was resized to 1024 × 1024 pixel without preserving the aspect ratio. In Table To evaluate our approaches for multi-label pathology classification, the entire corpus of ChestX-ray14  classifier only with the three non-image feature as input. The MLP classifier has a low average AUC of 0.61 but this still indicates that those non-image features could help to improve classification results when provided to our novel model architecture.20. Within each split, the data is divided into 70% training, 10% validation, and 20% testing. When working with deep learning, hyper-parameters, and tuning without a validation set and/or cross-validation can easily result in over-fitting. Since individual patients have multiple follow-up acquisitions, all data from a patient is assigned to a single subset only. This leads to a large patient number diversity . We estimate the average validation loss over all re-samples to determine the best models. Finally, our results are calculated for each fold on the test set and averaged afterwards.For an assessment of the generalization performance, we perform a 5 times re-sampling schemeet al.8 in Section 3.1.To have a fair comparison to other groups, we conduct an additional evaluation using the best performing architecture with different depth on the official split of Wang et al.3. At training, we sample various sized patches of the image with sizes between 8% and 100% of the image area. The aspect ratio is distributed evenly between 3:4 and 4:3. In addition, we employ random rotations between ±7° and horizontal flipping. For validation and testing, we rescale images to 256 × 256 and 480 × 480 pixels for small and large spatial size, respectively. Afterwards, we use the center crop as input image. As in the work of He et al.5, dropout is not employed21. As optimizer, we use ADAM22 with default parameters for β1 = 0.9 and β2 = 0.999. The learning rate lr is set to lr = 0.001 and lr = 0.01 for transfer-learning and from scratch, respectively. While training, we reduce the learning rate by a factor of 2 when the validation loss does not improve. Due to model architecture variations, we use batch sizes of 16 and 8 for transfer-learning and from scratch with a large input size, respectively. The models are implemented in CNTK and trained on GTX 1080 GPUs yielding a processing time of around 10 ms per image.In all experiments, we use a fixed setup. To extend ChestX-ray14, we use the same geometric data augmentation as in the work of Szegedy 23 to gain more insight into our CNNs. Grad-CAM is a method for visually assessing CNN model predictions. The method highlights important regions in the input image for a specific classification result by using the gradient of the final convolutional layer.Table The results indicate a high variability of the outcome with respect to the selected dataset split. Especially for “Hernia”, which is the class with the smallest number of positive samples, we observe a standard deviation of up to 0.05. As a result, an assessment of existing approaches and comparison of their performance is difficult, since prior work focuses mostly on a single (random) split.With respect to the different initialization schemes, we observe already reasonable results for OTS networks that are optimized on natural images. Using fine-tuning, the results are improved considerably, from 0.730 to 0.819 AUC on average. A complete training of the ResNet-50-1channel using CXRs results in a rather comparable performance. Only the high-resolution variant of the ResNet-50-large outperforms the FT approach by 0.002 on average AUC. In particular, for smaller pathologies like nodules and masses an improvement is observed , while for other pathologies a similar, or slightly lower performance is estimated.Finally, all our experiments with non-image features slightly increase the AUC on average to its counterpart (i.e. without non-image feature). Our from scratch trained ResNet-50-large-meta yields the best overall performance with 0.822 average AUC.To get a better insight why the non-image features only slightly increased the AUC for our fine-tuned and from scratch trained models, we investigated the capability of predict the non-image features based on the extracted image features. We used our from scratch trained model (i.e. ResNet-50-large) as a feature extractor and trained three models to predict the patient age, patient gender, and view position (VP) – i.e. ResNet-50-large-age, ResNet-50-large-gender, ResNet-50-large-VP. We employed the same training setup as in our experiments before. First, our ResNet-50-large-VP model can predict with a very high AUC of 0.9983 ± 0.0002 the correct VP . After choosing the optimal threshold based on Youden index, we calculated a sensitivity and specificity of 99.3% and 99.1%, respectively. Secondly, the ResNet-50-large-gender predicts the patient gender also very precisely with a high AUC of 0.9435 ± 0.0067. The sensitivity and specificity with 87.8% and 85.9% is also high. Finally, to evaluate the performance of the ResNet-50-large-age we report the mean absolute error (MAE) with standard deviation because age prediction is a regression task. The model achieved a mean absolute error of 9.13 ± 7.05 years. The results show that the image features already encode information about the non-image features. This might be the reason that our proposed model architecture with the non-image features at hand did not increased the performance by a large margin.Furthermore, the similarity between the trained models in terms of their predictions was investigated. Therefore, Spearman’s rank correlation coefficient was computed for the predictions of all model pairs, and averaged over the folds. The pairwise correlations coefficients for the models are given in Table 8 and the medical interpretation of the label are an important issue. As mention by Luke Oakden-Rayner24, the class “pneumothorax” is often labeled for already treated cases (i.e. a drain is visible in the image which is used to tread the pneumothorax) in the ChestX-ray14 dataset. We employ Grad-CAM to get an insight, if our trained CNN picked up the drain as a main feature for “pneumothorax”. Grad-CAM visualizes the areas which are most responsible for the final prediction as a heatmap. In Fig. While our proposed network architecture achieves high AUC values in all categories of the ChestX-ray14 dataset, the applicability of such a technology in a clinical environment depends considerably on the availability of data for model training and evaluation. In particular, for the NIH dataset the reported label noiseIn our evaluation, we noticed a considerable spread of the results in terms of AUC values. Next to the employed data splits, this could be attributed to the (random) initialization of the models, and the stochastic nature of the optimization process.et al.12 reported state-of-the-art results for all 14 classes on their own split. In Fig. et al. and other groups. For our model, we plot the minimum and maximum AUC over all re-samplings as error bars to illustrate the effect of random splitting. We achieve state-of-the-art results for “effusion” and “consolidation” when directly comparing our AUC (i.e. averaged over 5 times re-sampling) to former state-of-the-art results. Comparing the maximum AUC over all re-sampling splits results in state-of-the-art performance for “effusion”, “pneumonia”, “consolidation”, “edema”, and “hernia” and indicates that a fair comparison between groups without the same splitting might be non-conclusive.When ChestX-ray14 was made publicly available, only images and no official dataset splitting was released. Hence, researcher started to train and test their proposed methods on their own dataset split. We noticed a large diversity in performance with different splits of our re-sampling. Therefore, a direct comparison to other groups might be miss leading in the sense of state-of-the-art results. For example, Rajpurkar et al.8 released an official split of the ChestX-ray14 dataset. To have a fair comparison to other groups, we report results on this split for our best performing architecture with different depths – ResNet-38-large-meta, ResNet-50-large-meta, and ResNet-101-large-meta – in Table et al.8 and Yao et al.13 because Guendel et al.14 used an additional dataset – PLCO dataset25 – with 185,000 images. While the ResNet-101-large-meta already has a higher average AUC with 0.785 and in 12 out of 14 classes a higher individual AUC, the performance is compared to our ResNet-38-large-meta and ResNet-50-larg-meta lower. Reducing the number of layers increased the averaged AUC from 0.785 to 0.795 and 0.806 for ResNet50-large-meta and ResNet38-larg-meta, respectively. Hence, our results indicate that training a model with less parameter on Chest-Xray14 is beneficial for the overall performance. Secondly, Guendel et al.14 reported state-of-the-art results for the official split in all 14 classes with an averaged AUC of 0.807. While our ResNet-38-large-meta is trained with 185,000 images less, it still achieved state-of-the-art results for “Emphysema”, “Edema”, “Hernia”, “Consolidation”, and “Pleural Thicken.” and a slight less average AUC of 0.806.Later, Wang We present a systematic evaluation of different approaches for CNN-based X-ray classification on ChestX-ray14. While satisfactory results are obtained with networks optimized on the ImageNet dataset, the best overall results can be reported for the model that is exclusively trained with CXRs and incorporates non-image data .et al.14 . For other classes even higher scores are reported in the literature . However, a comparison of the different CNN methods with respect to their performance is inherently difficult, as most evaluations have been performed on individual (random) partitions of the datasets. We observed substantial variability in the results when different splits are considered. This becomes especially apparent for “Hernia”, the class with the fewest samples in the dataset  labels and their precise medical interpretation may be a limiting factor addition to the presence of treated findings. Our Grad-CAM results proves Oakden- Rayner’s concerns about the “pneumothorax” label. In a clinical setting, i.e. for the detection of critical findings, the focus would be on the reliably identification of acute cases of pneumothorax, while a network trained on ChestX-ray14 would also respond to cases with a chest drain.While the obtained results suggest that the training of deep neural networks in the medical domain is a viable option as more and more public datasets become available, the practical use of deep learning in clinical practice is still an open issue. In particular, for the ChestX-ray14 datasets, the rather high label noiseFuture work will include investigation of other model architectures, new architectures for leveraging label dependencies and incorporating segmentation information.


The in vitro enzymatic degradation of cross-linked poly(trimethylene carbonate) networks (PTMC-Ns) was performed in lipase solutions at 37 °C, and the effect of the initial molecular weight and cross-linker amount as well as the cross-linker type on the degradation rate of PTMC-Ns was investigated. Due to their denser structure and more hydrophobic surface as well as the higher glass transition temperature, a slower degradation rate was seen for PTMC-Ns with high initial molecular weight at a given cross-linker amount. Similar results could be observed as the cross-linker amount increased, and cross-linker type also influenced the degradation rate of PTMC-Ns. Furthermore, the enzymatic degradation of PTMC-Ns was accelerated by the surfactants role of lipase via surface erosion mechanism, the enzymatic degradation rate was higher than that of hydrolysis case. The results indicated that PTMC-Ns were promising candidates for clinical subcutaneous implants, especially due to their tunable degradation rate and enhanced form-stability as well as no acidic degradation products. As one of the most important biodegradable polymers, poly(trimethylene carbonate) (PTMC) has attracted considerable attention in recent years due to its favorable characteristics, such as well-documented biocompatibility, excellent biodegradability and rubber-like properties ,2,3. On 2 × day) by varying the TMC to CL ratio and the irradiation dose [Cross-linking normally gives polymers stable dimensions and enhanced corrosion resistance ,23, whicion dose . The Gamion dose ,29. In oion dose , and preion dose . Both reThermomyces lanuginosus, ≥100,000 U/g) is a good model for the in vivo erosion of PTMC-based materials [Thermomyces lanuginosus, ≥100,000 U/g) at 37 °C, was studied in more details. The effect of initial molecular weight and cross-linking density as well as the cross-linker type on degradation of PTMC-Ns was investigated, and the degradation rate and form-stability were monitored during the enzymatic degradation process. The hydrolytic degradation was also performed in pH = 7.4 phosphate buffered saline (PBS) to elucidate the role of enzymes in the degradation of PTMC-Ns.Lipase  and lipase solutions  were used as received from Sigma-Aldrich. All other solvents and reagents used were of analytical grades and purified by standard methods.TMC was purchased from Daigang Biomaterial Co., Ltd , recrystallized twice from acetic ether and dried for 24 h at 37 °C under reduced pressure before polymerization. Stannous octoate [Sn(Oct)The initial molecular weight (Mn) was determined by GPC  with a Waters Model 1515 isocratic high-performance liquid chromatography pump, a Waters Model 2414 differential refractive index detector and a Waters Styragel HT4 chromatographic column using THF as the solvent and a flow rate of 1 mL/min at 35 °C. The water contact angles of the different PTMC-Ns were determined using a contact angle-measuring instrument  equipped with a video measuring system was used to determine static contact angles of sessile drops (each based on 6 different films) at room temperature. The phase transition temperature of PTMC-Ns was determined with a Netzsch DSC 200 F3  equipped with a liquid nitrogen cooling system. The measured temperature range was between −100 and 100 °C, and the heating rate was 10 °C/min under nitrogen atmosphere. The thermal stability of the polymers under nitrogen atmosphere used a Netzsch TGA 209 F3  from room temperature to 550 °C and a heating rate of 10 °C/min. The mechanical properties of the networks were measured using an Instron 1121 universal testing machine  with a crosshead speed of 50 mm/min in accordance with GB/T 1040.1-2006. The tests were done on triplicate samples, and the results were presented as an average of the tested samples. The scanning electron microscopy (SEM) images were obtained with a XL30ESEM–FEG microscope . The sample surfaces were coated with Au to avoid charging. The pH of the media before and after degradation was monitored with a combination microelectrode  and a digital pH meter .2 as the catalyst. Under nitrogen atmosphere, dried TMC and cross-linker with anhydrous toluene solution of Sn(Oct)2 were added to glass ampoules containing clean and dry Teflon tubes with an inner diameter of 3 mm; The toluene was removed afterwards by evacuation. The ampoules were purged with dry nitrogen, heat-sealed under vacuum and vigorously shaken to obtain a homogeneous mixture of the monomers and the catalyst. Then, the ampoules were conditioned in an oil bath pre-heated to 130 ± 2 °C for 24 h. After the reaction, the ampoules were quenched at room temperature, and the PTMC-Ns rods were easily removed from the Teflon tubes and used without any further purification.The PTMC-Ns rods were directly synthesized via bulk ring-opening polymerization reaction of TMC with cross-linker bis(trimethylene carbonate) (BTB) or bis(ε-caprolactone) (BCP) in the presence of Sn(Oct)w0) and kept in a sealed flask containing chloroform. At regular intervals, the sample was removed and the excess solvent was blotted. The sample was then weighed and returned to the medium. This procedure was continued until a constant weight was attained (w). The gel percentages were determined gravimetrically after the swollen gels were dried to constant weights (w1). The gel percentage and swelling degree were calculated according to Equations (1) and (2), respectively.w0 is the initial weight of the samples before swelling, w1 is the final weight of the dried (extracted) samples, and w is the weight of the swollen samples. These measurements for gel percentage and swelling degree were done in triplicate for each network sample.To determine the gel percentage and swelling degree of the resulting PTMC-Ns, a piece of network sample was weighed , which were refreshed twice a week. The degradation experiments were performed in triplicate at 37 °C with gentle shaking. At regular time intervals, the PTMC-Ns rods were removed from the degradation media and washed with distilled water. After blotted with a tissue, the specimens were weighted and then vacuum dried at 37 °C to a constant weight for further analysis, and the diameter of the dried specimens was recorded. The pH of the media containing degradation products was also measured. The mass loss and loss in diameter were calculated via the following equations:iw, ww and dw represent the initial weight, wet weight and dry weight of the samples, respectively; and id and dd represent the initial diameter and final diameter of the dry samples after degradation, respectively.According to previous reports ,21,31, lAs a control, the hydrolytic degradation of the PTMC-Ns rods was carried out similarly with the enzymatic degradation, except that the medium was pH = 7.4 PBS solutions in absence of lipase.To investigate the effect of initial molecular weight and cross-linking amount as well as the cross-linker type on degradation, PTMC-Ns were fabricated in different cross-linking conditions, as described in our previous works . BrieflyAs shown in Thermomyces lanuginosus, ≥100,000 U/g) at 37 °C. It has been reported that lipase is one of the most efficient enzymes in eroding PTMC polymers . The enz256-0, PTMC256-0.05, PTMC256-0.1 and PTMC256-0.5 was 46.3 ± 0.4%, 33.6 ± 1.8%, 30.4 ± 0.4% and 29.8 ± 0.03%, respectively, after 12 weeks. Furthermore, the PTMC initial molecular weight also had conspicuous effect on the enzymatic degradation behavior of the PTMC-Ns. As shown in 72-0.1 and PTMC135-0.1 displayed a mass loss of 26.6 ± 0.2% and 29.6 ± 1.6%, respectively, after 12 weeks, whereas the mass loss for PTMC329-0.1 was 33.7 ± 0.05% at the same time point.It was clearly seen that the mass loss of the PTMC-Ns was dependent on the cross-linker amount and the initial molecular weight. k is the degradation rate constant and tM is mass loss of PTMC-Ns at the degradation time of t.To quantitatively elucidate the degradation behavior, the degradation rate constant of the PTMC-Ns was calculated from the mass loss based on a first-order kinetic model, and the following equation was adopted:256-0 was 3.81 %/w, which was higher than 2.76 %/w of the 0.05 mol % BTB cross-linked PTMC network (PTMC256-0.05). Then, the degradation rate of the PTMC256-Ns further decreased to 2.48 %/w as the cross-linker amount reached to 0.5 mol %. The results indicate that cross-linking could result in networks with much reduced degradation rate [256-Ns cross-linker by 0.05–0.5 mol % BTB was in a range of 80.94 ± 3.23%–97.89 ± 1.1%, indicating the formation of much denser networks that make the carbonate bonds less accessible for enzymatic hydrolysis.The results determined from linear fitting of the data in the plots of mass loss versus the degradation time  are listion rate , as the 256-Ns in chloroform decreased from 3943 ± 103% to 498 ± 16% with the increase of BTB amount from 0.05 to 0.5 mol %. This indicated that the formed networks were densely cross-linked, almost no space so that water could easily penetrate into the networks. Thus, the water uptake of the PTMC256-Ns decreased with the increase of BTB amount (256-Ns cross-linked by higher BTB amount enabled it to be degraded slower.It is well known that the mobility of the polymer chain is an important factor that influences the polymers degradation . The denB amount . As a re72-0.1) was 2.16 %/w, and then it was increased to 2.44 %/w for PTMC135-0.1 as the initial molecular weight reached to 135 kg/mol. As the initial molecular weight further increased to 256 and 329 kg/mol, the degradation rate of PTMC256-0.1 and PTMC329-0.1 was 2.52 %/w and 2.78 %/w, respectively; obviously, the higher the initial molecular weight, the greater the degradation rate of the PTMC-Ns.The in vitro enzymatic degradation rate of the PTMC-Ns seemed to be initial molecular weight dependent. As shown in This phenomenon was related to the hydrophilicity, which has an important effect on the polymer degradation behavior . As show256-0.1 cross-linked by BTB or BCP increased gradually with degradation. The linear relationships between the mass loss and degradation time were determined for the both cases, and the degradation rate calculated based on Equation (4) was 2.52 %/w (R = 0.996) and 2.67 %/w (R = 0.998), respectively. The degradation rate of the BTB cross-linked PTMC256-0.1 was slightly lower than that of the BCP cross-linked ones. It was attributed to the difference in the gel percentages of the two groups as shown in Tg induced reduction in the degradation rate. Here, we concluded that the BTB was more suitable to be utilized widely to fabricate the strong elastomeric polymer network with low degradation rate for biomedical applications.The effect of the cross-linker type on the degradation behavior of the resulting PTMC-Ns was also investigated, and the results were displayed in The results of the degradation rate of the PTMC-Ns stated above strongly demonstrated that the chemical cross-linking was an efficient strategy to reduce the degradation rate of PTMC via the manipulation of the cross-linker amount, initial molecular weight as well as the cross-linker type, allowing their extensive use in the long-term biomedical applications.329-0.1 before and after degradation. The original samples of PTMC329-0.1 exhibited a smooth surface before enzymatic degradation. However, the surface became rough after 1 week of degradation, and a highly hollow structure was detected after four weeks, the size and deepness of the hollows observed on the surface increased with the incubation time, as illustrated in The SEM measurements were performed to monitor the degradation of the resulting PTMC-Ns. 256-0, indicating that the surfaces had been extensively eroded. The pits on the surface of PTMC256-0.1 and PTMC256-0.5 became much shallower as indicated by the smaller roughness. That indicates the reduction in the erosion extent of the resulting networks with increasing cross-linker amount is very clear. Similar results were observed as the initial molecular weight decreased. The SEM results were coincidence with that of the mass loss.The form-stability of biodegradable polymers greatly influences their utility in the final biomedical devices, especially in those where deformation is not desired, e.g., the implant carriers for controlled drug release. Here, the form-stability of the resulting PTMC-Ns in vitro enzymatic degradation was determined via macroscopic observation.72-0 and PTMC135-0) splayed poor form-stability, the shape of the polymer rods was not cylindrical anymore, especially for the PTMC72-0 that underwent the worst deformation, and began changing its shape from cylinder to a highly sphere at week 1. Obviously, the corresponding cross-linked PTMC-Ns presented much better form-stability than the uncross-linked ones. As shown in 72-0.1 and PTMC135-0.1 retained their initial appearance and no deformation was observed during the degradation process. Therefore, we deduced that cross-linking plays an important role in enhancing the form-stability of the PTMC-based polymers. In our previous works [256-0 and PTMC329-0) could sustain their forms during the in vitro enzymatic degradation process, indicating their good form-stability. In this study, no difference was determined in the form-stability between the uncross-linked and cross-linked PTMC-Ns with high initial molecular weight (the figures were not displayed). The results illustrated that the cross-linking influenced the form-stability of the low-molecular-weight PTMC more significantly.us works , we founAs seen in 256-0.1.The linear relationship between mass loss and loss in diameter, shown in 72-0 and PTMC135-0) became smaller as they presented an appearance of oblate or sphere  without enzyme was also performed. The hydrolytic degradation of PTMC256-0.1 lost its mass much faster with lipase than without lipase. After 12 weeks, the mass loss of PTMC256-0.1 incubated in lipase was 30.4 ± 0.37%, whereas it only lost 5.20 ± 0.67% of its mass after 50 weeks of degradation without lipase. The degradation rate constant of PTMC256-0.1 in the presence and in the absence of the lipase were 2.52 and 0.098 %/w, respectively. The former was more than 25 times higher than the latter. The rapid surface erosion of PTMC-Ns in enzymatic conditions as well as stable under hydrolytic conditions suggested that enzymes played an important role in the degradation process. It was attributed to the fact that the lipase could act as a surfactant to interact with the degradation products of PTMC-Ns and accelerate them dispersing into the surrounding media. We concluded that the lipase could accelerate the degradation of PTMC-Ns by enhancing the loss of the degradation products, as observed in the in vitro degradation of the PTMC in our separate studies [It is clearly seen that the PTMC studies .It has been reported that the acidic degradation compounds are not produced upon the degradation of PTMC ,2,21. AsThe in vitro enzymatic degradation behavior of PTMC-Ns was quantitatively studied. The results illustrated that the degradation of PTMC-Ns was dependent on the initial molecular weight. Higher mass loss and subsequent higher degradation rate were detected for PTMC-Ns with higher initial molecular weight at a given cross-linker amount. The degradation rate of PTMC-Ns could also be tailored by the adjustment of cross-linker amount: the higher the cross-linker amount, the slower the degradation rate. Furthermore, the cross-linker type was also a valid means to tune the degradation rate of PTMC-Ns. The degradation rate of PTMC-Ns was slower than that of the linear ones, indicating the enhanced resistance to degradation of PTMC via cross-linking.PTMC-Ns maintained better form-stability versus the non-crosslinked ones during the enzymatic degradation, indicating that cross-linking was an effective strategy to enhance the form-stability of PTMC. With the comparison of an in vitro hydrolysis case, the much faster degradation rate in lipase was attributed to the essential role of enzymes in the erosion of PTMC-Ns. We concluded that the lipase acted as a surfactant to promote the dissolution of the degradation products into the surrounding media and thus accelerate the degradation via surface erosion mechanism.As stated above, PTMC-Ns with enhanced form-stability and lowered degradation rate given by cross-linking are the promising candidates for potential clinical application in subcutaneous implants. The degradation behavior with no acidic degradation products would donate well biocompatibility to PTMC-Ns. Future studies will focus on the in vivo degradation behaviors of and the tissue response to PTMC-Ns.


Escherichia coli and Staphylococcus aureus indicated that the addition of Mino significantly improved the antibacterial activity of the wound dressings. The tight structure of CM-Chit gel prevented the burst release of Mino so that the dressings had antibacterial activity in a certain period of release time. Cell culture assay showed that the dressings had excellent cell biocompatibility. As new functional dressings, the prepared composite dressings had excellent potential in the clinical healing of wounds.As derivatives from marine natural biomaterials, alginate-based and chitosan-based biomaterials are commonly used in wound dressings. Calcium alginate fiber (CAF) dressings possess excellent absorption and unique gel forming performance, but the low bioactivity limits its application in wound healing. Carboxymethyl chitosan (CM-Chit) has excellent antibacterial activity, but the gel structure with weak mechanical properties restricts its application. In this study, minocycline (Mino)/CM-Chit solution was coated on the surface of plasma treated CAF needle-punched nonwovens, and then Mino loaded CM-Chit gel/CAF nonwovens composite dressings were fabricated by EDC/NHS (1-3-(3-dimethylaminopropyl)-3-ethylcarbodiimide hydrochloride/N-hydroxysuccinimide) crosslinking. The dressings had a porous composite structure, which allowed them to quickly absorb and store a large number of wound exudates. Skin-like tensile performance allowed the dressings to provide a better healing environment. Antibacterial assay against  As the skin substitute, the ideal wound dressings should possess the following functions: 1) be a barrier to protect wound from contamination by chemicals and microorganisms; 2) allow air exchange, which provides oxygen for cell growth and excreting carbon dioxide produced by cells; 3) remove wound exudates in time and maintain the moist microenvironment between wound, wound exudates and dressings; 4) be non-toxic and non-allergenic to wound [ be a bar allow ai remove w be non-tN, O-carboxymethylated chitosan, O-carboxymethylated chitosan and N-carboxymethylated chitosan. Carboxymethyl chitosan biomaterials can be fabricated in neutral water medium, thus maintaining the activity of active substances such as small molecule drugs, proteins and cells, which expands the application range of chitosan [Chitosan is a cationic marine natural polysaccharide obtained from chitin after deacetylation of the N-acetyl glucosamine groups. The chemical structure of chitosan is composed by D-glucosamine and N-acetyl-D-glucosamine by β (1-4) glycosidic linkages . Owing tchitosan ,24,25,26chitosan ,28,29,30chitosan ,32,33,342+ ions, sodium alginate is synthesized into insoluble calcium alginate with “egg-box” macromolecular structure [2+ ions chelated with M groups are easily replaced by Na+ ions in wound exudates and then form sodium alginate hydrogels, producing moist healing conditions which are proved to reduce wound healing times [Sodium Alginate is a linear marine natural polysaccharide composed of β-D-mannuronate (M) and α-D-guluronate (G) by 1, 4-glycosidic covalent linkages. These two monomers are combined in different amount and sequential distribution to form various molecular chain fragments, such as M-blocks (MMM) G-blocks (GGG) and G-M-blocks (GMGMG) . Sodium tructure ,37. Thustructure ,40,41,42ng times . The algng times ,45. CalcAlginate has no ability to promote cell proliferation, migration, differentiation and specific gene expression. Meanwhile, the weak mechanical performance makes it difficult for carboxymethyl chitosan to produce a scaffold structure. In order to overcome the limitation of individual biomaterials and prepare biopolymers with multiple functions, hydrogels, microfibers, membranes and films composed by alginate and carboxymethyl chitosan have been prepared and their potential applications as wound dressings in promoting wound healing have been investigated. Compared with dressings made of individual biopolymer, these composite dressings possess better healing-promoting performance ,46,47. TIn this study, we developed the composite dressings made of carboxymethyl chitosan (CM-Chit) gel and calcium alginate fiber (CAF) needle-punched nonwovens. The mechanical performance, the physicochemical properties, the cell compatibility and the antibacterial activity were characterized. Meanwhile, the drug-embedded carboxymethyl chitosan-alginate composite dressings were prepared to explore the potential of the composite dressings as a drug delivery system, which endowed the composite dressings with more functions. The results evidenced that the composite dressings could be used as functional compress to accelerate wound healing.O-isoacylurea was formed by coupling the EDC and carboxyl groups in CM-Chit macromolecular chains, and then, the amide bond was formed between the O-isoacylurea and the amino groups in the CM-Chit macromolecular chains, making the macromolecular chains crosslinked. The existence of NHS enhanced the stability of the crosslinked products and the residual EDC/NHS was removed after washing with Milli-Q water to reduce the biological toxicity of the composite wound dressings. As shown in As shown in w/v) to 4% (w/v), the solution viscosity improved from 0.05 PA s to 1.20 PA s. With the increase of macromolecules content in solution, the entanglement degree and interaction force between macromolecular chains increased. Especially when the solution concentration increased from 3% (w/v) to 4% (w/v), the solution viscosity increased greatly. When solution was sheared, the entanglement between macromolecular chains was destroyed, reducing the viscosity of the solution. With the increase of the shear rate, the degree of disruption of intermolecular entanglement increased. The effect of shear rate on the viscosity of solution with high concentrations solution was significantly higher than that of solution with low concentrations. When the shear rate was higher than 20 r/s, the viscosity of solution with high concentrations decreased obviously and tended to be stable. The surface tension was another important indicator of solution properties, which was closely related to the density and viscosity of solution. As shown in In the preparation of the composite wound dressings, the performance of solution, especially the rheological properties, greatly affects the structure and properties of dressings. The rheological properties of CM-Chit solution were shown in The optical cross-section morphology of the composite wound dressings was shown in As shown in w/v) to 4% (w/v), the mean pore size of the composite dressings gradually increased from 37.30 μm to 62.49 μm. The main distribution ranges of pore size also changed significantly. The main distribution range of pore size of the composite dressings prepared by CM-Chit solution with concentrations of 1% (w/v), 2% (w/v), 3% (w/v) and 4% (w/v) were 20–60 μm, 20–80 μm, 40–100 μm and 40–120 μm, respectively. With the increase of CM-Chit solution concentration, the number of pores with large size increased significantly. This was mainly because that with the decrease of CM-Chit permeability, the destruction of CAF nonwovens porous structure caused by CM-Chit gel structure was reduced, thus increasing the distribution of large pores in the composite dressings.As shown in −1, 2910 cm−1 and 1730 cm−1 were the characteristic peaks of O-H, C-H and -COOH groups, respectively. The absorption peaks at 1632 cm−1, 1555 cm−1 and 1402 cm−1 characterized the COO-, N-H and -CH2COOH groups, which indicated the transition of amino and hydroxyl groups in chitosan macromolecules caused by carboxymethylation. From the FTIR spectra of the crosslinked CM-Chit, the stretching vibration peaks of -COOH groups at 1730 cm−1 disappeared, and the absorption peaks of macromolecular chains at 1632 cm−1 and 1402 cm−1 decreased, indicating the decrease of carboxymethyl groups linked with amino groups. The characteristic peaks at 1550 cm−1, 1407 cm−1 and 1025 cm−1 moved to 1555 cm−1, 1402cm−1 and 1023cm−1, indicating the changes of N-H, C=O, C-N and other groups. With the increase of CM-Chit concentration, the number of macromolecular chains involved in crosslinking increased, and the peak strength of characteristic absorption peaks of -COOH group decreased gradually; meanwhile, the peak strength of characteristic absorption peaks of N-H and C-H groups increased gradually. After crosslinking, the interconnected macromolecular chains formed a compact aggregated structure. With the increase of CM-Chit concentration, the degree of entanglement and crosslinking between macromolecular chains increased, resulting in a significant increase in the crystal structure and the intensity of the diffraction peaks at 2θ = 13.282° and 2θ = 38.428°. The crystallinities of the composite dressings fabricated by CM-Chit solution with concentration of 1% (w/v), 2% (w/v), 3% (w/v) and 4% (w/v) were calculated as 21.96%, 23.98%, 26.16% and 34.27%, respectively.The chemical structure of the composite dressings was characterized by FTIR and XRD spectra. The functional groups structure of the composite dressings before and after crosslinking was shown in The solution absorption performance of the composite dressings was shown in w/v) to 4% (w/v), CM-Chit solution crosslinked and formed a homogeneous gel surface without CA fibers distribution, reducing the contact angle from 37.08° to 32.20°. Due to the structure of CAF, nonwovens surface was stable, and the contact angle of CAF nonwovens did not change with the increase of CM-Chit solution concentration.The contact angles of the surface of CM-Chit gel and CAF nonwovens were illustrated in The tensile properties of the composite dressings were shown in w/v) to 4% (w/v), the Mino loading rate of CM-Chit gel increased from 53% to 79.5%, indicating that tight macromolecular chains entanglement structure facilitated the drug loading.The absorbance of Mino-PBS solutions with concentrations of 100, 50, 25, 12.5, 6.25, 3.125 and 1.5625 ug/mL at 272 nm was measured by ultraviolet spectrophotometer. The standard curve of Mino concentration (C as y) and absorbance (A as x) was drawn and the standard equation was calculated shown in A. The stw/v), the drug release reached the dynamic balance at 120 h. The high CM-Chit concentration and tight gel network made it difficult for the loaded drugs to penetrate into the solution, thus prolonging the time of drug release and the effective drug concentration.The Mino release profile from the Mino loaded composite dressings was investigated and the release curves were illustrated in Escherichia coli (E. coli) and Staphylococcus aureus (S. aureus). The positive charge in CM-Chit macromolecules interacts with the negative charge on the surface of bacteria, destroying the cell membrane of bacteria and causing cell death [E. coli and S. aureus were 0.95 mm and 1.2 mm, respectively. Therefore, the composite dressings possessed slight antimicrobial activity against E. coli and S. aureus. When the composite dressings contained Mino with a low concentration of 2.5% (w/v), the widths of inhibition zones against E. coli and S. aureus expanded to 13.82 mm and 11.45 mm, respectively. The antimicrobial activity of the composite dressings was significantly improved by the loading of Mino, meanwhile the width of inhibition zone against S. aureus expanded to 18.8 mm with the Mino concentration increased to 10% (w/v).The loaded Mino was expected to improve the antimicrobial activity of the composite dressings. The antibacterial activity of CAF nonwovens, composite dressings and Mino loaded composite dressings were evaluated by inhibition zone after 24 h of bacterial culture. As shown in ll death . The widw/v) Mino were immersed in PBS to release. The antibacterial activity against S. aureus of the Mino loaded composite dressings (4% (w/v) CM-Chit, 10% (w/v) Mino) after 1, 3, 7 days release was investigated and illustrated in The composite dressings loaded with 10% (w/v) CM-Chit and composite dressings with 10% (w/v) Mino. With the increase of incubation time, the numbers of cells cultured on the three dressings increased significantly, showing that the fabricated dressings possessed better biocompatibility. The absorbance of cells cultured on the CM-Chit gel surface of the composite dressings was slightly higher than that of cells cultured on CAF nonwovens, which indicated that the composite dressings had better biocompatibility. Moreover, with the increase of CM-Chit concentration, the absorbance of cells cultured on the gel surface increased slightly  was provided from United Medical Technologies Co., Ltd. . Carboxymethyl chitosan (CM-Chit) was purchased from Yuanye Bio-Technology Co., Ltd. . Its degree of deacetylation and degree of substitution were 85% and 80% respectively. 1-3-(3-dimethylaminopropyl)-3-ethylcarbodiimide hydrochloride (EDC), N-hydroxysuccinimide (NHS) and minocycline hydrochloride were purchased from Sigma-Aldrich . Ethyl alcohol and sodium chloride were purchased from Aladdin . Reagents used in bacterial culture assay  were obtained from Thermo Scientific Oxoid . Reagents used in cell culture in vitro were purchased from Gibco . Cell Counting Kit (CCK8) was purchased from Gibco . Cell viability  test kit was procured from KeyGEN BioTECH Co. . The water purified by a Milli-Q water purification system  was used in all the experiments and the resistivity higher than 18.2 MΩ.2. Step 2: CM-Chit solution preparation and plasma treatment of CAF nonwovens. CM-Chit was dissolved in Milli-Q water and stirred to form a uniform solution. The fluidity and surface tension of CM-Chit solution determined the diffusion of the solution in nonwovens. The rheological behaviors of CM-Chit solution were measured by ARES rheometers . The surface tension of CM-Chit solution was measured and characterized by the surface tensiometer . In order to improve the affinity of CAF nonwovens and the diffusion of CM-Chit solution in nonwovens, surface oxygen plasma treatment on CAF nonwovens was carried out by low-temperature plasma treatment instrument , the treatment time was set as 20 s. Step 3: CM-Chit solution coating. CM-Chit solution was coated on the surface of CAF nonwovens using a small laboratory coating machine . The running speed of the scraper was set as 15 mm/s, and the distance between the scraper and the surface of CAF nonwovens was set as 1 mm. Step 4: CM-Chit crosslinking. CAF nonwovens coated with CM-Chit solution was immersed in EDC/NHS solution (90% ethanol and 10% water) and placed at room temperature for crosslinking. After 24 h, the samples were taken out and washed with Milli-Q water for 3 times to remove excess EDC/NHS and ethanol.As shown in The surface and cross-section morphology of the lyophilized composite wound dressings were observed by a scanning electron microscope  at an acceleration voltage of 10kV. The porous structure of composite wound dressings, including the pore size and it distribution, was measured using the capillary flow porosimetry .−1. The crystal and aggregation structure of CM-Chit and CAF macromolecules were investigated by the D/max RB X-ray diffractometer  with Nickel-filtered Cu kα radiation in the 2θ range of 0–60°.The macromolecular structure of composite wound dressings was characterized by Fourier Transform Infrared (FTIR) and X-ray diffraction (XRD). The FTIR spectra were measured by Nicolet 6700 FTIR spectroscopy  in the attenuated total reflection mode (ATR). The absorption spectral range was 500–4000 cm0W) were immersed in Milli-Q water at 37 °C for 0.5, 1, 2, 4, 6, 8, 12, 24 h. The samples after absorption of water were weighed after removing the residual water (1W). The solution uptake rate (g/g) was calculated by (1 − W0W)/0W.The hydrophilicity of the composite wound dressings can be characterized by the contact angle. The contact angles the different surfaces of composite wound dressings were measured by the Contact Angle Tester . The samples  with a gauge length of 10 cm and a stretching speed of 50 mm/min.C was the drug concentration of the collected solution; V was the volume of the collected solution; m was the weight of Mino in CM-Chit solution.For drug loading, Mino was dissolved to CM-Chit solution and the mixture was stirred at room temperature in dark conditions to form uniform Mino/CM-Chit solution, and then Mino loaded CM-Chit gel/CAF nonwovens composite wound dressings were fabricated as described in n was number of displacement release solution; Cn was the concentration of drug in the release solution at nth release time interval, mg/mL; V was the volume of the release solution; Vi was the volume of drug release solution at the ith release time interval; Ci was the concentration of drug in the release solution at ith release time interval, mg/mL; 0V and 0C were 0; dm was the quality of drug in sample.The drug release from the Mino loaded CM-Chit gel/CAF nonwovens composite wound dressings was investigated in accordance with the methods described in the previous research . A totalEscherichia coli (E. coli), a gram-positive bacterium, and Staphylococcus aureus (S. aureus), a gram-positive bacterium, were used in this assay. The frozen E. coli and S. aureus were activated and cultured in the trypticase soy broth (TSB) medium in an oscillation incubator with a temperature of 37 °C and a speed of 120 rpm. The bacterium/TSB suspension was spread on the agar plates and the sterilized circular samples (n = 3) with a diameter of 14 mm were placed on the spread bacterium/TSB suspension. The agar plates were incubated in a constant temperature incubator with a temperature of 37 °C. After 24 h, the diameter of the inhibition zone was calculated from the images of agar plates captured by the automatic colony counter . The width of inhibition zone (H) was calculated according to Equation (3):The antibacterial activity of the fabricated wound dressings was evaluated by the agar plate diffusion method. The Mino loaded wound dressings samples were placed in PBS for drug release. After 1, 3, 7 days, the antimicrobial activity of the samples was measured using the same method.v/v) high glucose DMEM, 10% (v/v) fetal bovine serum (FBS) and 1% (v/v) penicillin/streptomycin at 37 °C and 5% CO2. The sterilized wound dressings were placed in 24-well cell culture plates. The HUVECs (105 cells/well) were seeded on the samples and cultured in medium at 37 °C and 5% CO2.Human umbilical vein endothelial cells  were cultured in medium with 90%  at 450 nm of the supernatant in each well was measured by a microplate reader .The proliferation of cells on the wound dressings was measured by CCK-8 assay. After 1, 3 and 7 days of culture, the medium was removed and 400 μL CCK-8 test solution was added into the well and cultured in dark conditions at 37 °C and 5% CO2. After 1 h, the working solution was removed and the stained cells were washed by PBS and then visualized by confocal laser scanning microscope .After 1, 3 and 7 days of culture, the medium was removed and the cells were washed by PBS for 3 times. Calcein-AM working solution was added to stain the cells and incubated at 37 °C and 5% COIn this study, minocycline/carboxymethyl chitosan solution was coated on the surface of plasma treated calcium alginate fibers needle-punched nonwovens and then immersed in EDC/NHS crosslinking solution. CM-Chit was crosslinked to form gel, thereby Mino loaded CM-Chit gel/CAF nonwovens composite dressings were fabricated. SEM images showed that the composite dressings had a porous structure, which was composed of gel, gel/nonwovens composite structure and nonwovens porous structure. With the increase of CM-Chit solution concentration, the thickness of gel structure and the pore size distribution was changed. The dressings had excellent absorption performance and quickly reached the maximum absorption volume so that wound exudates could be quickly transferred from the wound. The excellent mechanical properties of CAF nonwovens greatly improved the mechanical properties of CM-Chit gel so that the composite dressings had similar mechanical properties to skin. The addition of Mino significantly increased the antimicrobial activity of the composite dressings, and the gel structure avoided the burst release of the drug, which enabled the drug in the effective concentration within a certain period of time to achieve the antibacterial effect. The cells cultured on the composite dressings had good growth and proliferation activity. As new functional dressings, due to the excellent absorption performance, skin-like mechanical properties, long-term antimicrobial activity, drug sustained-release properties and biocompatibility, the prepared composite dressings had great potential in wound healing.


Marine microbes play a central role in driving biogeochemical cycles. Microbial extracellular enzymatic activities (EEA) are the ‘gatekeeper’ of the marine carbon cycle, and these enzymes may be found attached to cells or dissolved (cell-free). Recent studies indicated that the proportion of dissolved enzymatic activity is generally similar to (if not higher than) cell-attached activity. Thus, it is critical to understand the sources and sinks of cell-free EEA in the ocean. We herein empirically tested whether bacterial stress and mortality (induced by mitomycin C) are a source of the cell-free EEA of alkaline phosphatase (APase), beta-glucosidase (BGase), and leucine aminopeptidase (LAPase). We found that bacterial stress and mortality caused relative increases in the proportion of dissolved relative to total EEA of up to 10.5% for APase, 13.5% for BGase, and 7.3% for LAPase. These relative increases in dissolved EEA corresponded to absolute increases in the cell-free pool of 4.8, 7.2, and 3.8% for APase, BGase and LAPase, respectively. Collectively, our results contribute relevant information on the origin of free dissolved extracellular enzymes in marine waters, indicating that bacterial stress and mortality are a source of cell-free enzymatic activity and suggesting a potential link between microbial interactions and the degradation of organic matter via the release of cell-free enzymes. MITO-C is an antibiotic that inhibits DNA replication and activates DNA repair mechanisms that induce lysogenic viruses to enter the lytic cycle . Thus, M−1 final concentration) , and passed through a 0.8-μm polycarbonate filter (to remove grazers and big particles) at <100 mbar vacuum. This water was then used to prepare different treatments: (A) 0.8 μm-filtered seawater control, and (B) 0.8 μm-filtered seawater with stimulated bacterial mortality  . The pro2, and kept at −80°C until analyzed.Flow cytometry after nucleic acid staining was used to enumerate bacteria and viruses , 18, 27.versus side scatter  in the dark for 10 min. Prokaryotes were enumerated on an Attune flow cytometer  by their signature in a plot of green fluorescence  scatter , 18.Viral abundance was also measured by flow cytometry after SYBR Green I staining . Prior tin situ temperature for 1.5–3 h. An increase in fluorescence over time was transformed into hydrolytic activity using a standard curve established with different concentrations of the fluorochromes MUF and MCA added to 0.22 μm-filtered sample water. A final substrate concentration of 250 μmol L−1 was used to measure BGase, APase, and LAPase activities.The hydrolysis of the fluorogenic substrate analogues L-leucine-7-amido-4-methylcoumarin, 4-methylumbelliferyl (MUF)-β-D-glucoside, and MUF-phosphate was investigated to estimate the potential activity rates of LAPase, BGase, and APase, respectively . The samThe total and dissolved fractions of EEA were distinguished as previously described –9. Bulk i.e., %Bdiss–%Adiss), where A refers to the control and B is the mitomycin-stressed treatment, and Adiss and Bdiss are the corresponding dissolved EEA fractions of each . However, this representation of subtracting %Adiss from %Bdiss does not reflect the reduction in (inhibition of) EEA (and BA) caused by MITO-C. Thus, this representation was corrected by calculating the contribution of Adiss to B and subtracting this contribution from %Bdiss  to provide a more realistic view of the response. Although this representation provides information on the relative change in cell-free relative to total EEA, it merely gives a partial view of the response because it is possible to detect an increase in % values even if there is no increase in the pool of cell-free enzymes by mortality. Therefore, since this representation of the response does not reflect the (absolute) change in the pool size of cell-free enzymes by mortality, we then calculated absolute changes in the pool of cell-free enzymes by subtracting Adiss from Bdiss and calculating the % of the difference increase in the activity of dissolved enzymes due to mitomycin bacterial mortality relative to Control A .The effects of bacterial mortality and cell-free EEA were calculated for both the ‘relative proportion of cell-free to total EEA’ and as the ‘absolute change in the cell-free pool’. At the same time, the ‘relative proportion of cell-free to total EEA’ was presented in terms of the % increase in the contribution of cell-free enzymes to total enzyme activity by bacterial mortality relative to the contribution of cell-free enzymes to total enzymes in the control .In all analyses, parametric assumptions were initially checked using the Shapiro–Wilk test for normality and Levene’s test for equal variance. Where appropriate, data were log-transformed to meet normality assumptions prior to analyses. Tukey’s HSD tests were performed to assess the significance of differences between treatments. All analyses were run using JMP6 cells mL−1) was similar to that with the MITO-C treatment (1.2×106 cells mL−1) (At time zero (approximately 1 h after the addition of MITO-C), abundance in the control (1.3×10ls mL−1) . Viral als mL−1) , showingP<0.05, Tukey HSD) 5-fold increase in bacterial numbers was observed in the control, contrasting with an actual decrease of approximately 10% in bacterial numbers with the MITO-C treatment  (7 viruses mL−1)  in the MITO-C treatment.After 24 h, a significant  . Despitees mL−1) . This ca6 cells mL−1) in the control, but had begun to increase (from 1.1×106 to 4.6×106 cells mL−1) in the MITO-C treatment  increased to 1.6-fold that with the MITO-C treatment , and by 2.5-fold that of the control   . These rP>0.05, Tukey’s HSD), suggesting that although there was a change in the relative contribution of dissolved to total EEA, the absolute change in the pool size of cell-free enzymes induced by mortality was not significant.Since bacterial growth inhibition and mortality were detected with the MITO-C treatment (particularly in the first 24 h), the next step was to analyze potential EEA responses. The most active EEA was LAPase, followed by APase and BGase , which ica. 40%), and clearly detected for all enzymes  in cell-free EEA  were observed for APase  of approximately 5.4 and 4.3% of total APase and BGase activities.After 24 h, the inhibitory effect caused by MITO-C on EEA was more pronounced   was observed  in cell-free EEA were found for all three enzymes  . Thus, aobserved . Signifi enzymes , and 5C. enzymes . Taking i.e., Adiss) decreased for all enzymes from t0 to t48  or bottom-up  control are more likely to rely, at least initially, on cell-associated than cell-free enzymes.The percentage of dissolved relative to total EEA in the control  decreased  for APase, and not for the BGase or LAPase , and 5. 1 at t48 ; whereas  affect the cell-free EEA pool, indicating another link between microbial communities and organic matter degradation. Thus, since EEA is the rate limiting step in the degradation of organic matter, and cell-free EEA are a main component of total EEA, our results provide another crucial step towards understanding the forces that control energy flow in the ocean as well as the cycling of compounds that influence climate change, and may contribute towards building more accurate models of global biogeochemical cycles.Overall, no significant differences were observed in the dissolved EEA:bacterial abundance ratio of the MITO-C treatments relative to the control for any of the enzymes at t0, . However


Oxidative stress is a risk factor for lifestyle-related diseases, such as cancer. Investigations of the factors that increase or decrease oxidative stress contribute to disease prevention. In the present study, we focused on the 8-hydroxyguanine (8-OHGua) in saliva, as a new oxidative stress biomarker. The relationship between lifestyles and salivary 8-OHGua levels in 541 Japanese subjects was analyzed. The salivary 8-OHGua levels were significantly elevated in older persons, as well as those who smoke, have hypertension, or excess visceral fat. By contrast, statistically significant lower levels of 8-OHGua were observed in persons who moderately exercised or recently drank green tea or coffee. The direct collection of saliva, without any special collecting device, was suitable for the 8-OHGua analysis. The present results suggest that oxidative stress can be measured in a non-invasive manner with easily collectable saliva, and the salivary 8-OHGua may be a useful biomarker for lifestyle-related disease prevention. One of the most representative oxidative stress markers is 8-hydroxy-2'-deoxyguanosine (8-OHdG), which reflects the oxidative damage of the nucleobase, and has been widely analyzed using non-invasively collected urine samples. It is well known that urinary 8-OHdG values are increased by aging, smoking, alcohol consumption, and other factors. The free base, 8-hydroxyguanine . The 8-OHdG level in saliva is difficult to measure accurately, due to its low concentration. Several salivary biomarkers are reportedly useful in the diagnoses of systemic diseases, such as cardiovascular lesions, cancer, and diabetes,,10 but few studies have investigated the relationship between lifestyle and salivary antioxidant capacity. The aim of this study was to evaluate the relationship between oxidative stress and lifestyle in workers, by measuring the salivary 8-OHGua levels. Saliva has been drawing attention as a biological monitoring matrix, especially because of its easy and noninvasive collection. However, the measurement results are crucially dependent on the sampling method. One high priority issue to be solved is the potential interaction of the material in the collection device with the target analyte. Therefore, the material composition of the saliva collection device was also evaluated in this study.Oxidative stress is involved in lifestyle-related diseases, such as cancer. Ten min after rinsing the mouth with water, 2 ml of saliva were collected in three different ways, as follows: direct collection into a polypropylene tube [passive drool (PD)], commercially available material , and surgical cotton . In order to detect the absorption of 8-OHGua to the collection materials, samples of about 5 ml of PD saliva were collected from 7 persons. The collected saliva was divided into 3 portions. The saliva-soaked SOS or cotton samples were left to adsorb for 10 min at room temperature. The absorbed saliva was recovered from the soaked SOS or cotton by centrifugation at 3,500 rpm for 10 min.A total of 635 volunteers  aged 18–64, from three companies in Japan, participated in the study. After excluding samples because of sputum or blood contamination in the saliva, 541 subjects  were selected for analysis. After rinsing the mouth with water, about 2 ml of PD saliva was collected into a polypropylene tube in the morning (8 A.M.–11 A.M.). The collected saliva samples were kept in an ice cooler box during the sample collection, and then frozen at −30°C until analysis. At the same time, the following information was obtained by a questionnaire. The survey covered items such as gender, age, smoking status, alcohol consumption, green tea or coffee intake, daily physical activity, hypertension, waist circumference, and body weight gain compared with weight at age at 20.2SO4). After filtration through a centrifugal filter , a 20 µl portion of the filtrate was analyzed by an HPLC system to determine the 8-OHGua concentration. Measurements of 8-OHGua levels were performed based on the method of Kawai et al.Each saliva sample was stored at −30°C until analysis. Fifteen µl (Experiment 1) or 5 µl (Experiment 2) of Proteinase K  were added to 300 µl (Experiment 1) or 100 µl (Experiment 2) of saliva. The mixture was incubated at 37°C for 1 h. The mixture was evaporated to dryness by centrifugal vacuum concentration, and the residue was dissolved in 300 µl of dilution solution . The Wilcoxon signed-rank test was used for experiment 1. The statistical methods for experiment 2 are shown in each figure. The statistical significance level was set at 5% (p<0.05).All statistical analyses were performed with the EZR statistical software ,The salivary 8-OHGua measurement results were affected by the usage of the saliva absorption materials, such as SOS or surgical cotton Fig. a. In theAs the accuracy of the measurement, the coefficient of variation calculated based on the 8-OHGua standard solution was within 5%. The mean 8-OHGua level  in 541 subjects was 31.9 . There was no significant difference between male and female. A weak positive correlation was found between age and salivary 8-OHGua level , progesterone, and estradiol were artifactually increased by the use of a cotton-based collection material. In contrast, glutathione and sIgA (secretory Immunogloblin A) levels were reduced by cotton. The use of a polypropylene-based material lowered the cortisol levels in samples. In saliva samples collected with synthetic material, the levels of amylase, IgA, and lactoferrin were low. For the accurate measurement of 8-OHGua, the appropriate collection methods were evaluated at the beginning of this study. As shown in Fig. When saliva is used as a biomonitoring matrix, the material in the collecting device is one of the major factors influencing the levels of biomarkers. For example, the levels of thiobarbituric acid reacting substances,,19 The practical effectiveness of salivary 8-OHGua as a new oxidative stress marker was examined in workers, in experiment 2. ROS seem to be one of the key factors in aging. The urinary 8-OHGua and 8-OHdG levels increased with age in mice. In humans, oxidative damage biomarkers of DNA (8-OHdG), lipids , and proteins (carbonyl compounds) in skeletal muscle increased age-dependently. The 8-OHdG levels in leukocyte DNA and urine were significantly higher in elderly subjects. In this study, a relatively weak positive relationship was observed between age and salivary 8-OHGua in the scatter plots. In the cases separated by age (50 years old), the salivary 8-OHGua levels were significantly higher in the group of subjects aged 50 or over. The salivary 8-OHGua levels may be associated with aging. These results suggested that the salivary 8-OHGua level may be a useful biomarker of aging. Several molecular epidemiologic studies revealed that oxidative damage plays a certain role in inducing human lung cancer by smoking. The 8-OHdG levels in sperm were higher in smokers. The Brinkman index and the 8-OHdG level in lung tissue DNA showed a positive correlation. The urinary 8-OHdG levels were also higher in smokers. Our finding of the positive relation between the salivary 8-OHGua levels and the Brinkman index agreed well with previous studies. Regarding the alcohol-related diseases, several cohort studies have shown a dose-dependent increase in cirrhosis risk with high alcohol consumption (40 g/day for women and 60 g/day for men). On the other hand, the mortality of moderate drinkers (less than 23 g/day) was lower than that of nondrinkers. Several reports have shown a J-shaped relationship between alcohol consumption and related diseases.,30 Interestingly, the variations of the salivary 8-OHGua levels in this study were quite similar to those in previous reports. Alcohol-induced oxidative stress is a major cause of related diseases. Many kinds of antioxidants, such as an astaxanthin, lycopene, green tea polyphenols, green tea aroma, and coffee, were reported to reduce oxidative stress. In the present study, the salivary 8-OHGua levels were decreased by green tea or coffee consumption. Moderate physical activity also reduced oxidative stress.,38 At the same time, GSH (Glutathione-SH), catalase, glutathione peroxidase activity,,39 and total antioxidant capacity were increased. The salivary 8-OHGua levelswere significantly lower in persons who performed moderate physical activity. Metabolic syndrome, which involves several metabolic risk factors such as obesity, insulin resistance, hypertension, and dyslipidemia, has become a serious problem in developed countries. Many reports support the idea that increased oxidative stress plays an important role in metabolic syndrome-related diseases. The present findings of high salivary 8-OHGua in hypertensives are consistent with previous studies that suggested a link between oxidative stress and hypertension.,41 Therefore, the salivary 8-OHGua level may reflect the oxidative stress due to hypertension. Based on data from 2013, it was estimated that 4.5 million deaths worldwide were caused by excessive weight and obesity. The IARC  working group concluded that the absence of excess body fat lowers the risk of most cancers. In this study, the salivary 8-OHGua levels were significantly higher in the subjects above the standard Japanese diagnostic criteria (waist circumference and weight gain) for metabolic syndrome. The salivary 8-OHGua may be a suitable index reflecting the oxidative stress due to obesity.Reactive oxygen species (ROS) are implicated as a cause of cancer and lifestyle-related diseases.To the best of our knowledge, this is the first study to investigate the relationship between workers’ lifestyles and the levels of salivary 8-OHGua as an oxidative stress biomarker. Among the several biological fluids potentially available for oxidative stress measurement, saliva has been attracting interest for a long time, because of its non-invasive collection. The avoidance of mental and physical pain during sampling provides benefits to the subjects, medical service workers, and researchers. The best advantage is that saliva contains an adequate amount of 8-OHGua for the accurate measurement. These reasons highlight the benefits of using the combination of saliva as a sample and 8-OHGua as an oxidative stress marker. On the basis of our results, the salivary 8-OHGua is a useful biomarker to evaluate the oxidative stress for the prevention of lifestyle-related diseases.SW, YK, and KK collected the samples and data. SW statistically analyzed the data. KK and SW designed and critically discussed the study.


Background In many works, the chemical composition of bacterially-produced elemental selenium nanoparticles (Se0-nanoparticles) was investigated using electron dispersive X-ray analysis. The results suggest that these particles should be associated with organic compounds. However, a complete analysis of their chemical composition is still missing. Aiming at identifying organic compounds associated with the Se0-nanoparticles produced by the purple phototrophic bacteria Rhodospirillum rubrum and Rhodobacter capsulatus (α group of the proteobacteria), we used MALDI-TOF spectrometry.Results This technic revealed that numerous signals obtained from particles produced by both species of bacteria were from metabolites of the photosynthetic system. Furthermore, not only bacteriochlorophyll a, bacteriopheophytin a, and bacteriopheophorbide a, which are known to accumulate in stationary phase cultures of these bacteria grown phototrophically in the absence of selenite, were identified. The particles were also associated with intermediary metabolites of the bacteriochlorophyll a biosynthesis pathway such as protoporphyrin IX, protoporphyrin IX monomethyl ester, bacteriochlorophyllide a and, most likely, Mg-protoporphyrin IX-monomethyl ester, as well as with oxidation products of the substrates of protochlorophyllide reductase and chlorin reductase.Conclusion Accumulation of intermediary metabolites of the bacteriochlorophyll biosynthesis pathway in these purple phototrophic bacteria was attributed to inhibition of oxygen-sensitive enzymes involved in this pathway. Consistent with this interpretation it has been reported that these bacteria reduce selenite intracellularly, that they contain high levels of glutathione and that the reduction of selenite with glutathione is a very fast reaction accompanied by the production of reactive oxygen species. As many enzymes involved in the biosynthesis of bacteriochlorophyll contain [Fe-S] clusters in their active site, which are known to be degraded in the presence of reactive oxygen species as well as in the presence of molecular oxygen, we concluded that the substrates of these enzymes accumulate in cells during selenite reduction.0-nanoparticles produced by Rhodospirillum rubrum and Rhodobacter capsulatus is proposed to result from coating of the nanoparticles with the intracytoplasmic membrane of these bacteria, where the photochemical apparatus is concentrated.Association of metabolites of bacteriochlorophyll biosynthesis and degradation with the SeThe online version of this article (10.1186/s12866-018-1209-5) contains supplementary material, which is available to authorized users. In purple phototrophic bacteria selenite reduction and formation of Se0-nanoparticles was reported to occur intracellularly  clusters at their active site, which were shown to be very unstable in the presence of molecular oxygen and ROS such as superoxide anions and hydrogen peroxide + were expected to occur in the MS-spectra.As the first and second protonation steps of pyro-Phe ectively , the pH 3, and PPIX-O-CH 3 yielded protonated molecular ions with odd mass unit values. By contrast, signals attributed to bacteriochlorin derivatives such as BPhe a, BChlide a, and BPheide a most often appeared as molecular ion radicals with even mass unit values , appeared as protonated molecular ions or molecular ion radicals, depending on the initial pH of the sample.In our experiments signals attributed to porphyrin molecules such as PPIX, PPIX-di-O-CH es Table . Signals0-nanoparticle samples obtained from cultures of R. rubrum and Rba. capsulatus prepared with the DHB-matrix are presented in Fig. ap and BChl ag (BChl a containing a geranyl-geranyl moiety) were underestimated due to the instability of these compounds when embedded in the acid DHB-matrix  Table , indicatap yielded a major product ion at m/z 632.2 corresponding to the loss of the phytyl moiety (278.3 m. u.) and representing BChlide a , of a [-CH 2-CH 2-COOH] fragment (73 m. u.) or of both fragments, yielding product ions at m/z 550.3 (±1 m. u.), m/z 537.3, and m/z 477.3, respectively , was assigned to BChlide a. Thus, the results of tandem MS-analysis confirmed the results of demetalation i.e. the presence of BChl ag in the Se0-nanoparticle samples isolated from the culture medium of R. rubrum. Furthermore, this precursor ion yielded additional signals at m/z 572.3 and m/z 499.3, i.e. signals which were shifted by 22 m. u. compared to the second and the fourth signals obtained for the reference BPhe ap  and under the acid condition used in the preparation of the samples, thus yielding BPhe ag. Furthermore, similarly to the fragmentation pattern obtained in the tandem MS of the reference BPhe ap, additional signals at m/z 550 (± 1 m. u.), m/z 537.3, and m/z 477.3, were present in the tandem MS of the MS-signal at m/z 882.6  which is present in this bacterial species, and was reported to reduce BPhe ag, but not BChl ag , the [-CH 2-COOCH 3], and the [-CH 2-CH 2-COOCH 3] group, respectively, which were lost from the propionyl side chains of the tetrapyrrole ring  were considered to result from the transfer of a hydrogen atom from the product ion to the fragment, accompanied by a McLafferty rearrangement in the product ion .β bond of the C13- or C17-propionyl moiety. Cleavage of the α or γ bond showed significantly less intense signals , BPheide a, PPIX-O-CH 3, and PPIX. Thus, they were considered to represent degradation products of BChl a or of BChl a biosynthesis intermediary metabolites  obtained in the MS-spectra of Sea (m/z 550.3) was hardly detected in the MS-spectra of the Se0-nanoparticle samples obtained from cultures of either R. rubrum and Rba. capsulatus despite the fact that this compound was reported to accumulate to significant amounts in aged cultures of Rba. sphaeroides ) and of a 31 m. u. fragment. This latter fragment was assumed to derive from the β cleavage of a [-CH 2-CH 2-OH] side chain likely present in the precursor ion  or to the α cleavage of the [-COOH] group  was also present in this tandem MS. The possible presence of a [-COOH] group in the analyte, together with the relatively high intensity of the signal obtained at m/z 491.3 and corresponding to the loss of 104 m. u. (two fragments) led to the assumption that the MS-signal at m/z 595.3 could represent a degradation product of a demetalated protochlorophyllide (PChlide) with substituents bound not only to the C13 and C17 carbon atoms, but also to the C15 carbon atom of the tetrapyrrole ring, hence containing an opened isocyclic ring. Indeed, the intensity of the fragment at m/z 491.3 was consistent with a large number of double fragment losses representing a total of 104 m. u. . It well agreed with the theoretical mass of 595.284 (M= C 35H39N4O5) calculated using a monoisotopic composition of the molecule, thus confirming the elemental composition proposed for this compound.Consistent with the proposed porphyrin structure, this molecule yielded an MS-signal with odd m/z value, i.e. corresponding to a protonated molecular ion, as obtained for the references PPIX and PPIX-O-CH However, as no reference was available for this compound, its chemical structure remains to be confirmed by further analyses.0-nanoparticle samples isolated from cultures of Rba. capsulatus  were also present in the tandem MS-specta of the MS-signal at m/z 616.2 but shifted by 6-7 m. u.  in the presence of the ROS produced during selenite reduction  calculated for the monoisotopic composition of the compound proposed in , which gives a theoretical mass of 610.279 m. u. On the other hand the low difference between the measured and the theoretical mass we obtained confirmed the elemental composition we proposed for this compound.Its exact mass as determined by MALDI-TOF mass spectrometry yielded 616.212 ± 0.005 m. u. (5 measurements). It closely agreed with a theoretical exact mass of 616.217 . The smallest reported diameter of 20 nm was measured for particles produced by Veillonella atypica  cluster present in its BchH subunit -clusters repair, in cultures of selenite , 83.a biosynthetic pathway were reported to be shielded from the cytoplasm  clusters of these first three oxygen-sensitive enzymes of the Mg-branch of the BChl ytoplasm –86. Consytoplasm  produced enzymes , while t2 and (BchX)2 of DPOR and COR, respectively (see Table ·) from H2O2 in the presence of Fe2+  clusters , 96 are ·  group bound to the C13-carbon atom and a [-CH2-COOCH3] group bound to the C15-carbon atom of the tetrapyrrole ring. Further reactions leading to the structure proposed in Fig. 7As the MS-signal at m/z 595.3 was shown to represent a metal-free compound Table , its tan7Rba. capsulatus, which does not possess a very efficient peroxidase system (see below), and, consequently, whose DPOR active site may be exposed to hydrogen peroxide and produce OH·. Methylation of this degradation compound can possibly be explained by the activity of a radical SAM protein. This protein is reported to catalyze diverse reactions, including unusual methylations -clusters contained in the active site of these enzymes. Molecular oxygen and ROS, which are known to be produced during the reduction of selenite with glutathione, were proposed to be responsible of [Fe-S]-clusters and intermediary metabolite degradations. This proposition is consistent with results reported in previous works, indicating that cysteine desulfurase (IscS), an enzyme involved in [Fe-S] cluster repair, is largely induced in cultures of E. coli grown in the presence of a toxic concentration of selenite.MALDI-TOF spectrometry complemented with demetalation experiments has been proven useful for identifying small metabolites associated with Sea, the substrate of BChl synthase, was also proposed to arise from damage of the cytoplasm-exposed substrate-binding site of this enzyme by ROS produced during selenite reduction, and by the reaction of selenite with the cysteine present in its substrate-binding site.Accumulation of BChlide 0–nanoparticles produced by R.rubrum and Rba. capsulatus were coated with components of the intracytoplasmic membrane. This proposition is strengthened by MS-analysis of organic particle extracts showing that, in addition to BChl a, BPhe a, BPheide a, and intermediary metabolites of the BChl biosynthesis pathway, the particles were associated with many compounds yielding masses comprised between m/z 700 and m/z 850 which, according to their mass and to their solubility in organic solvents, were assumed to represent lipids and, possibly, quinone derivatives.Altogether these results indicated that selenite toxicity significantly affects the function of the photosynthetic system of the purple phototrophic bacteria. They furthermore suggested that the SeAdditional file 1ap and BChl ap prepared using the DHB-matrix. (PDF 185 kb)Representative MS-spectra of the references BPhe Additional file 2ap and BChl ap prepared using the THAP-matrix. (PDF 169 kb)Representative MS-spectra of the references BPhe Additional file 30-nanoparticle samples isolated from cultures of R. rubrum. (PDF 116 kb)Matrix effect on the MS-spectra of native SeAdditional file 40-nanoparticles. Effect of the washing procedure on the composition of the particle samples. (PDF 163 kb)MS-spectra of detergent-washed SeAdditional file 5Schematic representation of tandem MS-spectra of MS-signals at m/z 786.6, m/z 808.6 and m/z 824.6. (PDF 157 kb)Additional file 60-nanoparticle samples obtained from cultures of R. rubrum. (PDF 202 kb)MS-spectra of organic solvent extracts from SeAdditional file 70-nanoparticle samples obtained from cultures of Rba. capsulatus. (PDF 189 kb)MS-spectra of organic solvent extracts from SeAdditional file 83. (PDF 114 kb)Tandem MS-spectrum of PPIX-di-O-CH